


"""simple docstring"""






import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_lowerCAmelCase							:    Tuple					   =	logging.get_logger(__name__)

_lowerCAmelCase							:    Optional[Any]					   =	{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
_lowerCAmelCase							:    Any					   =	[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     Tuple:
   '''simple docstring'''





   for attribute in key.split("."       ):
      _lowerCamelCase   :				List[Any]							=     getattr(lowerCamelCase_			,		lowerCamelCase_       )

   if weight_type is not None:
      _lowerCamelCase   :				Dict							=     getattr(lowerCamelCase_			,		lowerCamelCase_       ).shape
   else:
      _lowerCamelCase   :				Any							=     hf_pointer.shape

   assert hf_shape == value.shape, (
       F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
       F""" {value.shape} for {full_name}"""
   )

   if weight_type == "weight":
      _lowerCamelCase   :				Tuple							=     value
   elif weight_type == "weight_g":
      _lowerCamelCase   :				Optional[Any]							=     value
   elif weight_type == "weight_v":
      _lowerCamelCase   :				Optional[int]							=     value
   elif weight_type == "bias":
      _lowerCamelCase   :				Optional[int]							=     value
   else:
      _lowerCamelCase   :				int							=     value

   logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""       )







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     Dict:
   '''simple docstring'''





   _lowerCamelCase   :				int							=     []
   _lowerCamelCase   :				Union[str, Any]							=     fairseq_model.state_dict()

   _lowerCamelCase   :				Optional[Any]							=     hf_model.feature_extractor
   _lowerCamelCase   :				Any							=     hf_model.adapter

   for name, value in fairseq_dict.items():
      _lowerCamelCase   :				Any							=     False
      if "conv_layers" in name:
         load_conv_layer(
             lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_			,		hf_model.config.feat_extract_norm == "group"			,		)
         _lowerCamelCase   :				Dict							=     True
      elif any(x in name for x in ["adaptor", "w2v_encoder.proj.", "w2v_proj_ln."]       ):
         load_adapter(lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_       )
         _lowerCamelCase   :				Union[str, Any]							=     True
      else:
         for key, mapped_key in MAPPING.items():
            if key in name or key.split("w2v_model."       )[-1] == name.split("."       )[0]:
               _lowerCamelCase   :				List[str]							=     True
               if "*" in mapped_key:
                  _lowerCamelCase   :				Any							=     name.split(lowerCamelCase_       )[0].split("."       )[-2]
                  _lowerCamelCase   :				List[str]							=     mapped_key.replace("*"			,		lowerCamelCase_       )
               if "weight_g" in name:
                  _lowerCamelCase   :				Union[str, Any]							=     '''weight_g'''
               elif "weight_v" in name:
                  _lowerCamelCase   :				Union[str, Any]							=     '''weight_v'''
               elif "bias" in name:
                  _lowerCamelCase   :				Optional[Any]							=     '''bias'''
               elif "weight" in name:
                  _lowerCamelCase   :				Optional[int]							=     '''weight'''
               else:
                  _lowerCamelCase   :				Any							=     None
               set_recursively(lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_       )
            continue
      if not is_used:
         unused_weights.append(lowerCamelCase_       )

   logger.warning(F"""Unused weights: {unused_weights}"""       )







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     List[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				Optional[Any]							=     full_name.split("conv_layers."       )[-1]
   _lowerCamelCase   :				Union[str, Any]							=     name.split("."       )
   _lowerCamelCase   :				Optional[Any]							=     int(items[0]       )
   _lowerCamelCase   :				Any							=     int(items[1]       )

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
         )
         _lowerCamelCase   :				Optional[int]							=     value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
         )
         _lowerCamelCase   :				str							=     value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
             " found."
         )
         _lowerCamelCase   :				int							=     value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
         )
         _lowerCamelCase   :				Optional[Any]							=     value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
   else:
      unused_weights.append(lowerCamelCase_       )







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     Dict:
   '''simple docstring'''





   _lowerCamelCase   :				List[str]							=     full_name.split("adaptor."       )[-1]
   _lowerCamelCase   :				List[str]							=     name.split("."       )

   if items[1].isdigit():
      _lowerCamelCase   :				Any							=     int(items[1]       )
   else:
      _lowerCamelCase   :				Any							=     None

   if "adaptor" not in full_name:
      if "proj_ln" in full_name:
         # has to be layer norm
         if "bias" in name:
            assert (
                value.shape == adapter.proj_layer_norm.bias.data.shape
            ), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
            _lowerCamelCase   :				Optional[int]							=     value
            logger.info(F"""Adapter proj layer norm bias was initialized from {full_name}."""       )
         if "weight" in name:
            assert (
                value.shape == adapter.proj_layer_norm.weight.data.shape
            ), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
            _lowerCamelCase   :				Optional[Any]							=     value
      else:
         # has to be projection layer
         if "bias" in name:
            assert (
                value.shape == adapter.proj.bias.data.shape
            ), F"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
            _lowerCamelCase   :				List[str]							=     value
            logger.info(F"""Adapter proj layer bias was initialized from {full_name}."""       )
         if "weight" in name:
            assert (
                value.shape == adapter.proj.weight.data.shape
            ), F"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
            _lowerCamelCase   :				Dict							=     value
            logger.info(F"""Adapter proj layer weight was initialized from {full_name}."""       )
   elif isinstance(lowerCamelCase_			,		lowerCamelCase_       ):
      if "bias" in name:
         assert (
             value.shape == adapter.layers[layer_id].conv.bias.data.shape
         ), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
         _lowerCamelCase   :				Dict							=     value
         logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""       )
      elif "weight" in name:
         assert (
             value.shape == adapter.layers[layer_id].conv.weight.data.shape
         ), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
         _lowerCamelCase   :				str							=     value
         logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}."""       )
   else:
      unused_weights.append(lowerCamelCase_       )







def        lowerCamelCase_(		_lowerCamelCase       )    ->     Optional[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				Tuple							=     emb.weight.shape
   _lowerCamelCase   :				Any							=     nn.Linear(lowerCamelCase_			,		lowerCamelCase_			,		bias=lowerCamelCase_       )
   _lowerCamelCase   :				Optional[Any]							=     emb.weight.data
   return lin_layer







@torch.no_grad()
def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		)    ->     Optional[Any]:
   '''simple docstring'''





   _lowerCamelCase   :				Tuple							=     WavaVecaConfig.from_pretrained(
       lowerCamelCase_			,		add_adapter=lowerCamelCase_			,		adapter_stride=lowerCamelCase_			,		adapter_kernel_size=lowerCamelCase_			,		use_auth_token=lowerCamelCase_			,		output_hidden_size=lowerCamelCase_			,		)
   _lowerCamelCase   :				List[Any]							=     MBartConfig.from_pretrained(lowerCamelCase_       )

   # load model
   _lowerCamelCase   :				Optional[Any]							=     fairseq.checkpoint_utils.load_model_ensemble_and_task(
       [checkpoint_path]			,		arg_overrides={
           "config_yaml": config_yaml_path,
           "data": "/".join(dict_path.split("/"       )[:-1]       ),
           "w2v_path": checkpoint_path,
           "load_pretrained_decoder_from": None,
       }			,		)
   _lowerCamelCase   :				List[Any]							=     model[0].eval()

   # load feature extractor
   _lowerCamelCase   :				Union[str, Any]							=     WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_			,		use_auth_token=lowerCamelCase_       )

   # set weights for wav2vec2 encoder
   _lowerCamelCase   :				Dict							=     WavaVecaModel(lowerCamelCase_       )

   recursively_load_weights_wavaveca(model.encoder			,		lowerCamelCase_       )

   # load decoder weights
   _lowerCamelCase   :				Tuple							=     MBartForCausalLM(lowerCamelCase_       )
   _lowerCamelCase   :				Tuple							=     hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()			,		strict=lowerCamelCase_       )
   logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}"""       )
   logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""       )

   _lowerCamelCase   :				List[str]							=     SpeechEncoderDecoderModel(encoder=lowerCamelCase_			,		decoder=lowerCamelCase_       )
   _lowerCamelCase   :				Optional[Any]							=     False

   _lowerCamelCase   :				Tuple							=     MBartaaTokenizer(lowerCamelCase_       )
   tokenizer.save_pretrained(lowerCamelCase_       )

   _lowerCamelCase   :				List[str]							=     hf_wavavec.config.to_dict()
   _lowerCamelCase   :				Tuple							=     tokenizer.pad_token_id
   _lowerCamelCase   :				Tuple							=     tokenizer.bos_token_id
   _lowerCamelCase   :				Any							=     tokenizer.eos_token_id
   _lowerCamelCase   :				str							=     '''mbart50'''
   _lowerCamelCase   :				Union[str, Any]							=     '''wav2vec2'''

   _lowerCamelCase   :				Union[str, Any]							=     tokenizer.eos_token_id
   _lowerCamelCase   :				Optional[Any]							=     250004
   _lowerCamelCase   :				List[str]							=     tokenizer.eos_token_id

   _lowerCamelCase   :				Tuple							=     SpeechEncoderDecoderConfig.from_dict(lowerCamelCase_       )

   hf_wavavec.save_pretrained(lowerCamelCase_       )
   feature_extractor.save_pretrained(lowerCamelCase_       )


if __name__ == "__main__":
  _lowerCAmelCase							:    Optional[Any]					   =	argparse.ArgumentParser()
  parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
  parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
  parser.add_argument('''--config_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
  parser.add_argument(
      '''--encoder_config_path''',
      default='''facebook/wav2vec2-xls-r-1b''',
      type=str,
      help='''Path to hf encoder wav2vec2 checkpoint config''',
  )
  parser.add_argument(
      '''--decoder_config_path''',
      default='''facebook/mbart-large-50-one-to-many-mmt''',
      type=str,
      help='''Path to hf decoder checkpoint config''',
  )
  parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
  parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
  parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
  parser.add_argument('''--encoder_output_dim''', default=1024, type=int, help='''encoder output dim''')
  parser.add_argument('''--start_token_id''', default=25_0004, type=int, help='''`decoder_start_token_id` of model config''')

  _lowerCAmelCase							:    str					   =	parser.parse_args()
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.dict_path,
      args.config_yaml_path,
      encoder_config_path=args.encoder_config_path,
      decoder_config_path=args.decoder_config_path,
      add_adapter=args.add_adapter,
      adapter_kernel_size=args.adapter_kernel_size,
      adapter_stride=args.adapter_stride,
      decoder_start_token_id=args.start_token_id,
      encoder_output_dim=args.encoder_output_dim,
  )


import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


lowercase			:				List[str]   =     logging.get_logger(__name__)






def        snake_case__		(    lowerCamelCase_=None     ,				lowerCamelCase_=None		):
							return field(default_factory=lambda: default     ,				metadata=lowerCamelCase_		)







@dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		List[str]								=	list_field(
							    default=[]		,       metadata={
							        '''help''': (
							            '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
							            ''' of all available models'''
							        )
							    }		,       )

							UpperCAmelCase_						:		List[int]								=	list_field(
							    default=[8]		,       metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}				)

							UpperCAmelCase_						:		List[int]								=	list_field(
							    default=[8, 32, 1_28, 5_12]		,       metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}		,       )

							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Use FP16 to accelerate inference.'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Benchmark training of model'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Verbose memory tracing'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
							    }		,       )
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Trace memory line by line'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Save result to a CSV file'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Save all print statements in a log file'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to print environment information'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': (
							            '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
							            ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
							            ''' for debugging / testing and on TPU.'''
							        )
							    }		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'inference_time_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving time results to csv.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'inference_memory_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'train_time_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'train_memory_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'env_info_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving environment information.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'log_{round(time()				)}.csv'		,       metadata={'''help''': '''Log filename used if print statements are saved in log.'''}		,       )
							UpperCAmelCase_						:		int								=	field(default=3		,       metadata={'''help''': '''Times an experiment will be run.'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': (
							            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
							            ''' model weights.'''
							        )
							    }		,       )




							def       snake_case			(					self						)		->       Union[str, Any]:
														warnings.warn(
														    f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
														    ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
														    ''' to benchmark Transformer models.'''    ,						__UpperCAmelCase    ,						)




							def       snake_case			(					self						)		->       Union[str, Any]:
														return json.dumps(dataclasses.asdict(self						)    ,						indent=2						)




							@property
							def       snake_case			(					self						)		->       List[str]:
														if len(self.models						) <= 0:
																					raise ValueError(
																					    '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
																					    ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''						)
														return self.models






							@property
							def       snake_case			(					self						)		->       int:
														if not self.multi_process:
																					return False
														elif self.is_tpu:
																					logger.info('''Multiprocessing is currently not possible on TPU.'''						)
																					return False
														else:
																					return True






